Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM + ) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS’ coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCA_MG), with reference SCA maps (SCA_REF) generated from binary classification of TM imagery. SCA_MG showed strong agreement with SCA_REF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCA_REF and SCA_MG ranged between −2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCA_REF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCA_MG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91.In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km²) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.     

Integration of the MODIS Snow Cover Produced Into Snowmelt Runoff Modeling

Because of the difficulty of monitoring and measuring snow cover in mountainous watersheds, satellite images are used as an alternative to mapping snow cover to replace the ground operations in the watershed. Snow cover is one of the most important data in simulation snowmelt runoff. The daily snow cover maps are received from Moderate Resolution Imaging Spectroradiometer (MODIS), and are used in deriving the snow depletion curve, which is one of the input parameters of the snowmelt runoff model (SRM). Simulating Snowmelt runoff is presented using SRM model as one of the major applications of satellite images processing and extracting snow cover in the Ghara - Chay watershed. The first results of modeling process show that MODIS snow covered area product can be used for simulation and forecast of snowmelt runoff in Ghara - Chay watershed. The studies found that the SCA results were more reliable in the study area.     

MTSAT-2静止气象卫星中国区域雪盖监测

利用MTSAT-2静止气象卫星数据开展了中国区域的雪盖监测研究,结合MODIS雪盖产品及站点雪深观测数据对判识结果进行对比分析和验证。首先,根据MTSAT-2静止气象卫星数据特点,进行角度效应校正及多时相数据合成,以减少云对图像的影响;其次,根据多个雪盖判识因子建立中国区域雪盖判识算法;最后,对比分析2011年1月份MTSAT-2和MODIS雪盖判识结果,并使用站点观测数据进行精度验证。研究表明:(1)MTSAT-2雪盖判识受云影响比例约30%,MODIS雪盖产品受云影响比例约60%,MTSAT-2去云效果明显。(2)无云情况下,MTSAT-2雪盖判识和MODIS雪盖产品判识精度均高于92%;有云覆盖时,MTSAT-2判识精度约65%,优于MODIS雪盖产品35%的判识精度。(3)MTSAT-2静止气象卫星在保持高积雪判识精度的前提下,可以更有效减少云对雪盖判识影响,实时获取更多地表真实信息。该研究对中国区域雪盖信息准确监测、气候变化研究以及防灾减灾等具有重要意义。     

基于遥感数据的积雪信息提取:以新疆北疆为例(英文)

This paper proposes an applicable approach for snow information abstraction in northern Xinjiang Basin using MODIS data. Linear spectral mixture analysis (LSMA) was used to calculate snow cover fractions (SF) within a pixel, which was used to establish a regression function with NDSI. In addition, 80 snow depths samples were collected in the study region. The correlation between image spectra reflectance and snow depth as well as the comparison between measured snow spectra and image spectra was analyzed. An algorithm was developed for snow depth inversion on the basis of the correlation between snow depth and snow spectra in the region. The results indicated that the model of SF had a high accuracy with the mean absolute error 0.06 tested by 26 true measured values and the validation for snow depth model using another dataset with 50 sampling sites showed an RMSE of 1.63. Our study showed that MODIS data provide an alternative method for snow information abstraction through development of algorithms suitable for local application. Supported by the National Natural Science Foundation of China (No.70361001).     

Production of global land cover data – GLCNMO

Abstract

Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and expert's comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.     

Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images.     

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.     

业务化MODIS BRDF模型对冰雪BRDF/反照率的反演能力评估

全球MODIS冰雪反照率产品在定量遥感中有着广泛应用,但由于该产品的业务化算法是建立在表征植被—土壤系统基础上的罗斯表层(RT)李氏稀疏互易核(LSR)的二向性反射分布函数(BRDF)模型(简称为RTLSR),因此该模型对冰雪的二向性反射及反照率的反演能力有待评估。本文基于地球反射极化和方向测量仪(POLDER)的多角度冰雪反射率数据,综合评估了RTLSR模型在表征冰雪二向反射及反演反照率等方面的能力。为量化评估结果,本研究基于渐进辐射传输(ART)模型,从POLDER冰雪数据中筛选出高质量数据,使用ART模型拟合的高质量结果作为参考,比较结果表明:(1)在表征冰雪方向性散射方面,RTLSR模型整体拟合精度较低。在1020 nm波段,其均方根误差(RMSE)最大可达到0.0498,相较于ART模型的拟合结果偏高了约53.70%;(2)在反演冰雪反照率方面,RTLSR模型与ART模型反演结果也存在差别,其决定系数为0.529,均方根误差为0.0333,偏差为-0.0274,基于RTLSR模型的反演结果低估了ART模型的反演结果。为了使核驱动模型能更准确地表征冰雪BRDF特征和反演反照率,该模型需要针对冰雪散射特点进行进一步的发展。     

Using cosmo-skymed images to detect wet snow cover on the Kraków Ice Field,King George Island,Antarctica

This work analysed the spatio-temporal variation of snow cover on the Kraków Ice Field, located in the King George Island, Antarctica. High spatial resolution images of COSMO-SkyMed were used in this study. These X-band images are vertically and horizontally co-polarized and their intensity data were converted to amplitude (dB). The COSMO-SkyMed images were classified by a minimum distance algorithm and post-classified based on knowledge of adjacency relationships of snow zones. Hypsometric, slope, aspect and solar radiation maps to support the interpretation of backscatter patterns in the COSMO-SkyMed images. Three radar zones were classified in these images: percolation, slush and wet snow radar zone. Positive surface air temperatures and rainfall events, registered from a meteorological station, lead to increase in wet snow and slush zones. The COSMO-SkyMed images and minimum distance algorithm were adequate to discriminate the snow cover and to assess the supraglacial melting pattern during the ablation season in the study area.     

高光谱遥感积雪制图算法及验证

雪盖面积是高山地区和季节雪盖区水文和气象模型的重要输入因子。机载和星载遥感数据提取的雪盖面积是融雪径流模型的重要组成部分。对应不同传感器件的光谱特征，多种分类方法被相继提出。但是，缺乏相对独立的验证手段来评价各种分类方法，其主要原因是缺乏地面真实状态。针对该现状，本研究利用高光谱图像的细分光谱特征，建立高光谱影像及其对应“地面真相”的像对数据库来发展和验证积雪制图算法，并展示MODIS积雪制图算法验证和ASTER混合像元分解雪盖制图算法研究的应用实例。     

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands,USA

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R² values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.     

A GIS-based method for defining snow zones: application to the western United States

This study maps the geographic extent of intermittent and seasonal snow cover in the western United States using thresholds of 2000–2010 average snow persistence derived from moderate resolution imaging spectroradiometer snow cover area data from 1 January to 3 July. Results show seasonal snow covers 13% of the region, and intermittent snow covers 25%. The lower elevation boundaries of intermittent and seasonal snow zones increase from north-west to south-east. Intermittent snow is primarily found where average winter land surface temperatures are above freezing, whereas seasonal snow is primarily where winter temperatures are below freezing. However, temperatures at the boundary between intermittent and seasonal snow exhibit high regional variability, with average winter seasonal snow zone temperatures above freezing in west coast mountain ranges. Snow cover extent at peak accumulation is most variable at the upper elevations of the intermittent snow zone, highlighting the sensitivity of this snow zone boundary to climate conditions.     

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km² river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.     

Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data

Snow cover monitoring in the Qinghai-Tibetan Plateau is very important to global climate change research. Because of the geographic distribution of ground meteorological stations in Qinghai-Tibetan Plateau is too sparse, satellite remote sensing became the only choice for snow cover monitoring in Qinghai-Tibetan Plateau. In this paper, multi-channel data from Visible and Infrared Radiometer (VIRR) on Chinese polar orbiting meteorological satellites Fengyun-3(FY-3) are utilized for snow cover monitoring, in this work, the distribution of snow cover is extracted from the normalized difference snow index(NDSI), and the multi-channel threshold from the brightness temperature difference in infrared channels. Then, the monitoring results of FY-3A and FY-3B are combined to generate the daily composited snow cover product. Finally, the snow cover products from MODIS and FY-3 are both verified by snow depth of meteorological station observations, result shows that the FY-3 products and MODIS products are basically consistent, the overall accuracy of FY-3 products is higher than MODIS products by nearly 1 %. And the cloud coverage rate of FY-3 products is less than MODIS by 2.64 %. This work indicates that FY-3/VIRR data can be reliable data sources for monitoring snow cover in the Qinghai-Tibetan Plateau.     

近10年新疆积雪面积时空变化研究

区域尺度积雪信息的时空监测对确定雪灾的影响范围及灾情等级划分具有重要意义。本文利用近10年的MODIS积雪产品,按月最大面积的规则合成;分析了新疆积雪覆盖面积的时空变化特征,结果表明:时间上,新疆积雪面积有减少的趋势。空间上,近10年新疆积雪季节内永久性积雪覆盖区域主要分布在阿勒泰山脉、天山北麓及沿昆仑山脉西南部。其中天山及阿尔泰山之间的河谷及盆地的草原积雪面积波动主导了新疆整体积雪总面积的波动。     

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Global land cover data could provide continuously updated cropland acreage and distribution information, which is essential to a wide range of applications over large geographical regions. Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products: MODIS land cover (MODISLC) at 500-m resolution in 2010, GlobCover at 300-m resolution in 2009, FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data. Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products, which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation (MDev) and RMSE, but were less effective on GlobCover product. We found that, in the USA, the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage, while FROM-GLC-agg gave the least deviation from the survey at the state level. Correlation between land cover map estimates and survey estimates is significant, but stronger at the state level than at the county level. In regions where most mismatches happen at the county level, MODIS tends to underestimate, whereas MERIS and Landsat images incline to overestimate. Those uncertainties should be taken into consideration in relevant applications. Excluding interannual and seasonal effects, R² of the FROM-GLC regression model increased from 0.1 to 0.4, and the slope is much closer to one. Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.     

An Introduction to MODISI and SCMOD Methods for Correction of the MODIS Snow Assessment Algorithm

Detection, monitoring and precise assessment of the snow covered regions is an important issue. Snow cover area and consequently the amount of runoff generated from snowmelt have a significant effect on water supply management. To precisely detect and monitor the snow covered area we need satellite images with suitable spatial and temporal resolutions where we usually lose one for the other. In this study, products of two sensors MODIS and ASTER both on board of TERRA platform having low and high spatial resolution respectively were used. The objective of the study was to modify the snow products of MODIS by using simultaneous images of ASTER. For this, MODIS snow index image with high temporal resolution were compared with that of ASTER, using regression and correlation analysis. To improve NDSI index two methods were developed. The first method generated from direct comparison of ASTER averaged NDSI with those of MODIS (MODISI). The second method generated by dividing MODIS NDSI index into 10 codes according to their percentage of surface cover and then compared the results with the difference between ASTER averaged and MODIS snow indices (SCMOD). Both methods were tested against some 16 MODIS pixels. It is found that the precision of the MODISI method was more than 96%. This for SCMOD was about 98%. The RMSE of both methods were as good as 0.02.     

基于MODIS数据的雪面温度遥感反演

On the basis of simplification of the Planck function in a low temperature range, this paper revises the practical split-window algorithm and presents a method for retrieving snow surface temperature （Ts） based on MODIS data in the middle-latitude region. The application of this method in Qinghai Lake region reveals that it is feasible for the retrieval of Ts. Results of correlation analysis indicate that there was strong negative relationship between Ts and altitude. By analyzing three typical areas in which land cover was relatively homogenous, this paper discusses the relationship between Ts and normalized difference snow index （NDSI） and then presents a new concept named ＂NDSI-Ts space＂.     

基于MODIS数据的天山北坡经济带雪深反演研究

根据实测雪深不同的积雪反射光谱值分析雪深和积雪反射率的关系,探讨利用MOD IS数据反演雪深的可行性和反演雪深的MOD IS最佳通道。利用2004年12月至2005年3月的MOD IS数据,以天山北坡经济带为实验区,结合该区域实测雪深数据、气象台地面雪深观测记录建立雪深反演数学模型,分析雪深空间分布特征对农业开发具有重要意义。     

MODIS影像雪深遥感反演特征参数选择与模型研究

在综合分析已有研究成果的基础上,选择MODIS遥感影像,借助灰色系统理论,结合观测站实测雪深数据,选择雪深反演特征参数,构建反演模型,并定义多元回归模型的综合评价系数,进而从构建的多个回归模型中,选择出雪深反演最优模型。